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Update app.py
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app.py
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@@ -2,108 +2,106 @@ import gradio as gr
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import time
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from transformers import pipeline
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#
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models = {
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"ChatGPT-like": pipeline(
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tokenizer="tiiuae/falcon-rw-1b",
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device=-1
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),
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"DeepSeek-like": pipeline(
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"text-generation",
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model="deepseek-ai/DeepSeek-Coder-1.3B-instruct",
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tokenizer="deepseek-ai/DeepSeek-Coder-1.3B-instruct",
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device=-1
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),
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"GigaChat-like": pipeline(
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"text-generation",
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model="tinkoff-ai/ruDialoGPT-medium",
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tokenizer="tinkoff-ai/ruDialoGPT-medium",
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device=-1
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)
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}
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# Промпты
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#
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def generate_classification(user_input):
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prompt_simple = build_simple_prompt(user_input)
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prompt_cot = build_cot_prompt(user_input)
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for name, pipe in models.items():
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# CoT
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start_cot = time.time()
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time_cot = round(time.time() - start_cot, 2)
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#
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start_simple = time.time()
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time_simple = round(time.time() - start_simple, 2)
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"
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"cot_time": time_cot,
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"
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"simple_time": time_simple
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}
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return (
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)
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# Интерфейс
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with gr.Blocks() as demo:
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gr.Markdown("##
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inp = gr.Textbox(label="Вопрос клиента", placeholder="Например: Я не могу
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btn = gr.Button("Сгенерировать")
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# ChatGPT-like
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gr.Markdown("### ChatGPT-like (
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cot1 = gr.Textbox(label="CoT ответ")
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cot1_time = gr.Textbox(label="Время CoT")
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simple1 = gr.Textbox(label="Обычный ответ")
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simple1_time = gr.Textbox(label="Время обычного")
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# DeepSeek-like
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gr.Markdown("### DeepSeek-like")
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cot2 = gr.Textbox(label="CoT ответ")
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cot2_time = gr.Textbox(label="Время CoT")
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simple2 = gr.Textbox(label="Обычный ответ")
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simple2_time = gr.Textbox(label="Время обычного")
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# GigaChat-like
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gr.Markdown("### GigaChat-like (
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cot3 = gr.Textbox(label="CoT ответ")
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cot3_time = gr.Textbox(label="Время CoT")
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simple3 = gr.Textbox(label="Обычный ответ")
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simple3_time = gr.Textbox(label="Время обычного")
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btn.click(
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cot1, cot1_time, simple1, simple1_time,
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cot2, cot2_time, simple2, simple2_time,
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cot3, cot3_time, simple3, simple3_time
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])
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import time
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from transformers import pipeline
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# Настройка моделей
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models = {
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"ChatGPT-like (FRED-T5)": pipeline("text2text-generation", model="ai-forever/FRED-T5-1.7B", tokenizer="ai-forever/FRED-T5-1.7B", device_map="auto"),
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"DeepSeek-like (Qwen7B)": pipeline("text-generation", model="lightblue/DeepSeek-R1-Distill-Qwen-7B-Multilingual", tokenizer="lightblue/DeepSeek-R1-Distill-Qwen-7B-Multilingual", device_map="auto"),
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"GigaChat-like (GigaChat-20B)": pipeline("text-generation", model="ai-sage/GigaChat-20B-A3B-instruct", tokenizer="ai-sage/GigaChat-20B-A3B-instruct", device_map="auto")
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}
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# Промпты
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cot_instruction = (
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"Ты — банковский специалист. Клиент задал вопрос."
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" Проанализируй обращение пошагово:
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1. В чём проблема?
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2. Возможные причины?
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3. Как решить?
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Вывод: категория обращения."
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)
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simple_instruction = "Ты — банковский специалист. Определи категорию обращения клиента кратко."
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# Генерация промптов
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def build_prompt(instruction, user_input):
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return f"{instruction}\n\nКлиент: {user_input}"
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# Обработка ответа
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def get_output(pipe, prompt, max_tokens=300):
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try:
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output = pipe(prompt, max_new_tokens=max_tokens, truncation=True, do_sample=True, temperature=0.7)[0]
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return output.get("generated_text") or output.get("output_text") or "(нет ответа)"
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except Exception as e:
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return f"Ошибка: {e}"
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# Основная функция
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def generate_comparison(user_input):
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result = {}
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prompt_cot = build_prompt(cot_instruction, user_input)
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prompt_simple = build_prompt(simple_instruction, user_input)
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for name, pipe in models.items():
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# CoT ответ
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start_cot = time.time()
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answer_cot = get_output(pipe, prompt_cot)
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time_cot = round(time.time() - start_cot, 2)
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# Обычный ответ
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start_simple = time.time()
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answer_simple = get_output(pipe, prompt_simple)
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time_simple = round(time.time() - start_simple, 2)
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result[name] = {
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"cot_answer": answer_cot.strip(),
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"cot_time": f"{time_cot} сек",
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"simple_answer": answer_simple.strip(),
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"simple_time": f"{time_simple} сек"
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}
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return (
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result["ChatGPT-like (FRED-T5)"]["cot_answer"], result["ChatGPT-like (FRED-T5)"]["cot_time"],
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result["ChatGPT-like (FRED-T5)"]["simple_answer"], result["ChatGPT-like (FRED-T5)"]["simple_time"],
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result["DeepSeek-like (Qwen7B)"]["cot_answer"], result["DeepSeek-like (Qwen7B)"]["cot_time"],
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result["DeepSeek-like (Qwen7B)"]["simple_answer"], result["DeepSeek-like (Qwen7B)"]["simple_time"],
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result["GigaChat-like (GigaChat-20B)"]["cot_answer"], result["GigaChat-like (GigaChat-20B)"]["cot_time"],
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result["GigaChat-like (GigaChat-20B)"]["simple_answer"], result["GigaChat-like (GigaChat-20B)"]["simple_time"]
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)
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# Интерфейс
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with gr.Blocks() as demo:
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gr.Markdown("## Сравнение моделей: ChatGPT, DeepSeek, GigaChat (банковская классификация)")
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inp = gr.Textbox(label="Вопрос клиента", placeholder="Например: Я не могу оплатить картой", lines=2)
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btn = gr.Button("Сгенерировать")
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# ChatGPT-like
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gr.Markdown("### ChatGPT-like (FRED-T5)")
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cot1 = gr.Textbox(label="CoT ответ")
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cot1_time = gr.Textbox(label="Время CoT")
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simple1 = gr.Textbox(label="Обычный ответ")
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simple1_time = gr.Textbox(label="Время обычного")
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# DeepSeek-like
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gr.Markdown("### DeepSeek-like (Qwen7B)")
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cot2 = gr.Textbox(label="CoT ответ")
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cot2_time = gr.Textbox(label="Время CoT")
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simple2 = gr.Textbox(label="Обычный ответ")
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simple2_time = gr.Textbox(label="Время обычного")
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# GigaChat-like
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gr.Markdown("### GigaChat-like (GigaChat-20B)")
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cot3 = gr.Textbox(label="CoT ответ")
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cot3_time = gr.Textbox(label="Время CoT")
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simple3 = gr.Textbox(label="Обычный ответ")
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simple3_time = gr.Textbox(label="Время обычного")
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btn.click(generate_comparison, inputs=[inp], outputs=[
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cot1, cot1_time, simple1, simple1_time,
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cot2, cot2_time, simple2, simple2_time,
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cot3, cot3_time, simple3, simple3_time
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])
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if __name__ == '__main__':
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demo.launch()
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